Lung cancer is a major cause of death worldwide, but early detection of lung cancer is a key factor for increasing survival rates of lung cancer patients. Currently, CT and bronchoscopy are the principal techniques used for lung cancer detection by identifying pulmonary nodules, which if certain predetermined criteria are met are then invasively biopsied to determine the pathology of the nodule(s). A recent U.S. National Lung Screening trial found that periodic computerized tomography (CT) screening of heavy smokers could reduce lung cancer mortalities by as much as 20%. However, this benefit is offset by the morbidity, cost, and occasional mortality incurred by pursuing nonmalignant pulmonary nodules and adenopathy. Thus, with the advent and increasing acceptance of computerized tomography (CT) scan screening for lung cancer, the importance of distinguishing benign from malignant intrathoracic disease is ever increasing. Accordingly, it is important to develop reliable methods that minimize the diagnostic burden to patients who have no significant disease while expediting treatment in patients who actually have lung cancer.
In recent years, the analysis of exhaled breath has become an international research frontier because of its applicability for noninvasive health diagnoses. Several approaches have been developed to analyze exhaled breath including the use of sensor arrays, proton-transfer reaction mass spectrometry (PTR-MS), selected ion flow tube mass spectrometry (SIFT-MS), and gas chromatography-mass spectrometry (GC-MS), to name a few. Although some VOCs in exhaled breath have been reported as potential lung cancer biomarkers, there has been no clinical adoption of breath analysis methods for diagnosis because of the lack of cancer specific VOC markers for reliably predicting lung cancer disease state.
Moreover, analyzing exhaled breath for cancer-indicating biomarkers, i.e., excreted metabolic products, is a daunting task, insofar as over 1700 endogenous volatile organic compounds (VOCs) have been identified in human breath. Additionally, many of these endogenous VOCs are present in exhaled breath in quantities that are less than the experimental error of the detection methods that are used to detect and/or identify them. For example, many of the VOCs in breath range from only a few parts per trillion (ppt) to a few parts per billion (ppb) concentration; many chemical species in breath samples are at millions-fold higher concentration than prevalent VOCs, such as water vapor and carbon dioxide, which may need to be removed to avoid swamping most analytical instruments. Additionally, breath is a chemically-diverse mixture containing analogue/homologue/isomeric mixtures of alcohols, ketones, and aldehydes, which complicate the identification of disease biomarkers; and VOCs in breath may include non-metabolic constituents, which may introduce false biomarkers in breath analysis.
Thus, in order to efficiently and accurately analyze VOCs in breath so as to detect or identify a disease state, there are multiple hurdles to overcome. The first hurdle to overcome is that of concentrating the VOCs of interest. General approaches to concentrating one or more VOCs of interest from dilute gaseous samples have focused on one of the following: chemical, cryogenic, and adsorptive methods. The second hurdle is identifying specific relationships between biomarker(s) and/or quantities of specific biomarkers, which can be correlated with a high level of certainty to the presence of the disease state, with a low chance of false-negatives.
Therefore, in view of the shortcomings and challenges with conventional methods of detecting/identifying and screening for lung cancer, there is a need for new non-invasive methods.